Data extraction and duplicate detection

ABSTRACT

A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of copending U.S. patent applicationSer. No. 16/185,207, entitled “DATA EXTRACTION AND DUPLICATE DETECTION”and filed on Nov. 9, 2018, which claims priority to and the benefit ofIndian Patent Application No. 201811036657, filed on Sep. 28, 2018 andentitled “DATA EXTRACTION AND DUPLICATE DETECTION,” both of which areincorporated by reference herein in their entireties for all purposes.

FIELD

The disclosure generally relates to computer systems, and morespecifically, to data extraction and document de-duplication.

BACKGROUND

Invoice processing is typically an integral part of a company'soperations. Invoice processing often involves extensive manual effortsat different stages such as, for example, scanning invoices, manuallyentering data into enterprise resource planning systems, de-duplicatingagainst previously paid invoices, etc. Invoices may be produced inmultiple formats such as, for example, PDFs, word documents, TIFF, etc.Furthermore, invoices may vary in styles or templates across differentvendors. Manually examining such variations and volumes of invoices forcorrectness, genuineness, and duplicates against historic invoices isusually highly subjective and increases the average cost and time toprocess an invoice. It is computationally unfeasible for many existingcomputer systems to detect one-to-many matches among invoices ofdifferent formats. As such, processing invoices from multiple sourcesand formats to organize the key information in a structured manner andidentify duplicates creates a technical problem.

SUMMARY

Systems, methods, and articles of manufacture (collectively, the“system”) for data extraction and invoice duplicate detection aredisclosed. The system may perform operations including receiving aninvoice; performing optical character recognition on the invoice;extracting a plurality of key-value pairs from the invoice; generating astructured template comprising the plurality of key-value pairs; forminga feature vector; determining, using a duplicate model and based on thefeature vector, that the invoice is a duplicate of a historic invoice ina historic invoice database; receiving an input to the duplicate modelindicating an accuracy of the duplicate determination; and modifying,based on the input, the duplicate model.

In various embodiments, forming the feature vector may compriseconcatenating similarity measures across different fields. The systemmay execute a word break algorithm to segment non-space separated wordswhich exist in a reference dictionary. The system may divide a pluralityof bigrams in the invoice into categories consisting of (1) both wordsin a reference dictionary; (2) only first word in the referencedictionary; (3) only second word in the reference dictionary; and (4)neither word in the reference dictionary. The system may perform a tableparsing operation on the invoice. The system may save a value andlocation of a field in the invoice. The system may create a lookupdictionary comprising description keywords.

The foregoing features and elements may be combined in variouscombinations without exclusivity, unless expressly indicated hereinotherwise. These features and elements as well as the operation of thedisclosed embodiments will become more apparent in light of thefollowing description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter of the present disclosure is particularly pointed outand distinctly claimed in the concluding portion of the specification. Amore complete understanding of the present disclosure, however, may beobtained by referring to the detailed description and claims whenconsidered in connection with the drawing figures, wherein like numeralsdenote like elements.

FIG. 1 illustrates a block diagram illustrating various exemplary systemcomponents of a system for data extraction and de-duplication, inaccordance with various embodiments.

FIG. 2 illustrates an exemplary process flow for data extraction, inaccordance with various embodiments.

FIG. 3 illustrates an example of a word break algorithm used as part ofthe text post-processing operations, in accordance with variousembodiments.

FIG. 4 illustrates an example of a string similarity based algorithmused as part of the text post-processing operations, in accordance withvarious embodiments.

FIG. 5 illustrates an exemplary process flow for data extraction, inaccordance with various embodiments.

FIG. 6 illustrates an exemplary process flow for duplicate invoicedetection, in accordance with various embodiments.

DETAILED DESCRIPTION

The system provides an end-to-end solution for invoice processing whichincludes reading invoices (both pdfs and images), extracting keyrelevant information from the face of invoices, organizing the relevantinformation in a structured template as a key-value pair, and comparinginvoices based on the similarities between different invoice fields toidentify potential duplicate invoices.

A customer (e.g., merchant) may receive invoices from one or morevendors. The invoices may be received in multiple formats (e.g., anative PDF, a paper invoice, a TIFF file, etc.). The customer may invokea data extraction module operating on one or more servers. For invoiceswhich are in a native PDF format, the data extraction module may convertthe PDF to text. The data extraction module may perform a structuringalgorithm to convert unstructured text to structured templates. Aduplicate detection module may compare the structured text with invoicesstored in a historic invoice repository.

The system may enable detection of duplicate invoices. In this regard,merchants may minimize computing resources used to evaluate whether apaper or digital file has been previously received or processed. Thismay also result in easier accounting, book keeping, expense reporting,reduced disputes, merchant returns, and/or the like. The system may alsoprovide for a reduction in disputes and a reduction in fraud, as adigital copy of documents, invoices, and/or other electronic data may beavailable in real time or soon after an invoice is received.

This process improves the functioning of the computer, and provides atechnical solution to the problem of processing invoices from multiplesources and formats to organize the key information in a structuredmanner and identify duplicates. For example, by using machine learningto streamline the invoice de-duplication process, the merchant systemperforms less computer functions and provides less input, which saves ondata storage and memory which speeds processing. Additionally, bytransmitting, storing, and accessing data using the processes describedherein, the security of the data is improved, which decreases the riskof the computer or network from being compromised. By identifyingduplicate invoices, this system saves resources and computer processingtime which otherwise would be required for repetitive payments of theduplicate invoices or later recovering the payments if identified asduplicates. Additionally, the system can prevent duplicate invoices fromgetting stored in the system, thereby reducing storage space usage.

Referring to FIG. 1, a system 100 for invoice de-duplication isdisclosed. In various embodiments, the system 100 may be computer based,and may comprise a processor, a tangible non-transitorycomputer-readable memory, and/or a network interface, along with othersuitable system software and hardware components. Instructions stored onthe tangible non-transitory memory may allow the system 100 to performvarious functions, as described herein. The system 100 may alsocontemplate uses in association with web services, utility computing,pervasive, and individualized computing, security and identitysolutions, autonomic computing, cloud computing, commodity computing,mobility and wireless solutions, open source, biometrics, grid computingand/or mesh computing.

In various embodiments, the system 100 may comprise a merchant system110. The merchant system 110 may be configured as a central hub toaccess various systems, engines, and components of the system 100. Inthat regard, the merchant system 110 may comprise a network, acomputer-based system, one or more servers, and/or software componentsconfigured to provide an access point to various systems, engines, andcomponents. The merchant system 110 may be in operative and/orelectronic communication with various networks 120, a historic invoicerepository 130, and one or more invoice sources, such as a supplierserver 141, a supplier client computer 142, or a physical invoice 143.

The merchant system 110 may be configured to convert the invoicesreceived from the invoice sources to text. The merchant system 110 mayextract key data fields from the invoices and organize the data in astructured manner. The merchant system 110 may compare the key datafields and identify duplicate invoices. Duplicate invoices may berejected and returned to the supplier. Unique invoices may be processedand stored in the historic invoice repository 130.

Referring to FIG. 2, a flowchart of an overview of the process for dataextraction is illustrated according to various embodiments. In general,a data extraction module may transform PDF invoices into structuredkey-value pairs. A merchant may receive a plurality of invoices, whichmay be from different vendors and in different formats (step 210). Forinvoices which are in a native PDF format, the merchant system mayconvert the PDF to text (step 222). A data extraction module may executea structuring algorithm (step 224). The structuring algorithm mayorganize each invoice line read from left-to-right. The data extractionmodule may perform text post-processing operations (step 226). The textpost-processing operations may comprise word completion and spellingcorrections.

For invoices which are not in a native PDF format, the data extractionmodule may convert the PDF to a JPG file (step 232). The data extractionmodule may perform OCR on the JPG file, such as by using Tesseract 4.0(step 234). The data extraction module may execute a structuringalgorithm (step 236). The structuring algorithm may leverage XML-typeoutput from OCR to preserve the structure of the text. The dataextraction module may perform text post-processing operations (step238). The text post-processing operations may comprise semantic textcleansing using language models.

The text post-processing operations may output a structure preservedlayout of the invoice text (step 240). The data extraction module mayextract relevant key-value pairs and save a structured template as a csvfile (step 250). The structured template may be generalized to differentinvoice templates from different vendors.

Referring to FIG. 3, an example of a word break algorithm used as partof the text post-processing operations is illustrated according tovarious embodiments. Exiting OCR processes may add spaces in incorrectlocations, as well as unnecessarily remove spaces, as shown in textblock 310. The word break algorithm may remove all spaces from the textblock 310, as shown in text block 320. The word break algorithm maysegment non-space separated words which exist in a reference dictionary,as shown in text block 330. The word break algorithm may segmentunigrams, as well as bigrams and trigrams. A bigram makes a predictionfor a word based on the one before, and a trigram makes a prediction forthe word based on the two words before that.

Referring to FIG. 4, an example of a string similarity based algorithmused as part of the text post-processing operations is illustratedaccording to various embodiments. The OCR process may introduce one ormore errors, such as an incorrect, missing, or additional letter, whichmay result in words which are not present in the reference dictionary,as shown in text block 410. The string similarity based algorithm maycategorize bigrams into one of four categories: (1) Both words indictionary, e.g., “Terms of” (2) Only first word in dictionary, e.g.,“Be drawnin” and “Billing Client” (3) Only second word in dictionary,e.g., “Ddescription terms” and “Accont number” and (4) Neither word indictionary, e.g., “Inv ice.” The data extraction module may reference alanguage model database 420 to identify the most similar bigrams, andthe language model may output the correct terms, as shown in text block430.

Referring to FIG. 5, a flowchart 500 of a process for data extraction isillustrated according to various embodiments. The process may include atleast one of regular expression (REGEX) based extraction (step 510),structure-aware extraction (step 520), table parsing (step 530), oraddress parsing (step 540).

In step 510, the data extraction module may create regular expressionsfor all possible variations of different fields, such as invoice number,amount, date, PO number, period, account number, etc. The dataextraction module may initialize the value of each field as default andprocess one line at a time. The data extraction module may applyregular-expression based extraction for each field if its value is equalto the default. The data extraction module may check if the extractedvalues are valid and satisfies the field criteria. If not, the dataextraction module may reassign the field value to the default. The dataextraction module may apply REGEX to each line in the invoice.

In step 520, the data extraction module may create a field lookupdictionary containing the relevant fields and their possible variations.The data extraction module may process each line of the invoice and lookfor keywords from the field lookup dictionary. If a line has validfields, the data extraction module may save the fields and theirlocations on the invoice. The data extraction module may createcandidate key-value pairs based on the spatial location of the fieldsand the candidate values in the following lines. The data extractionmodule may apply REGEX to each candidate key-value pair, and the dataextraction module may check if the extracted values are valid andsatisfy the field criteria.

In step 530, the data extraction module may create a lookup dictionarycontaining relevant and common description keywords. The data extractionmodule may extract phrases from document line and identify phrasescontaining description keywords from the lookup dictionary. For a lineto be classified as a valid table header, at least two phrases may havehigh overlap with description fields, e.g. “Date,” “Amount,” “InvoiceNumber,” “Description,” etc. The data extraction module may save allphrases as column headers along with their spatial locations. The dataextraction module may read the next line as a row and extract phrases.The data extraction module may assign phrases to different columns basedon the spatial location on the column headers and phrases. The dataextraction module may repeat this process for each line below the tableheaders until the phrases follow the spatial alignment with the columns.

In step 540, the data extraction module may create a lookup dictionarycontaining the relevant and common address keywords. The data extractionmodule may extract phrases from each line and identify valid addresskeywords. The data extraction module may assimilate phrases from thenext few lines which could potentially be a part of the correspondingaddress based on their spatial locations. The data extraction module maycreate candidate key-value pairs for each candidate. The data extractionmodule may validate each address by passing it through aregular-expression based address parser.

Referring to FIG. 6, a flowchart 600 of a process for duplicate invoicedetection is illustrated according to various embodiments. The processmay include receiving structured data from daily and historic invoices(step 610), data pre-processing (step 620), pair-wise similarity/featurevector generation (step 630), duplicate model building (step 640),applying domain rules (step 650), generating a potential duplicatereview report (step 660).

In step 620, the duplicate detection module may remove special characteror white-spaces from field values. The duplicate detection module maybin all addresses into four categories: bill-to, ship-to, remit-to, andothers. The duplicate detection module may standardize all dates,amounts, and other field values to a uniform format.

In step 630, the duplicate detection module may pair a test invoice withan invoice from the repository one at a time. The duplicate detectionmodule may compare corresponding fields across two invoices and computedifferent similarity measures. the duplicate detection module mayconcatenate all similarity measures across different fields to form afeature vector. The duplicate detection module may repeat this processto generate feature vectors by pairing each test invoice with invoicesform the repository.

In step 640, the duplicate detection module may assign a label {0, 1} tothe feature vector corresponding to each pair. The value may be a 1 ifthe pair is a duplicate pair, or a 0 if the pair is not a duplicate. Thefeature vectors and associated labels may be used as inputs to train aclassifying engine. The classifying engine may predict the label {1, 0}for unseen pairs of invoices based on their feature vectors, along withthe model reason code.

In step 650, the duplicate detection module may create a set of domainrules to identify duplicates based on the domain expertise from subjectmatter experts. The duplicate detection module may take the duplicatemodel's prediction about a pair and run it through the set of domainrules. The duplicate detection module may refine the duplicate model'sprediction if it conflicts with any of the domain rules.

In step 660, the duplicate detection module may provide a reportcontaining a potential duplicate pair. An analyst, which may be a humanor a machine, may evaluate the duplicate pairs and model reason codes toprovide feedback on the duplicate model's performance. The analyst mayprovide feedback on both false positives and false negatives. Thefeedback may be provided back to the duplicate model to update andre-train the duplicate model with the corrected labels for the pairs.

For invoices which are determined to be duplicates, the duplicatedetection module may reject the invoice, or flag the invoice for furtherreview, such that payment is not improperly sent to the supplier of theinvoice. For invoices which are determined to be unique, the duplicationdetection module may forward the invoice to an accounts payable systemfor payment to the supplier of the invoice. The invoice may then bestored in the historic invoice database.

By utilizing the processes described herein, entities may decrease theamount of processing capabilities required to compare new invoices toexisting invoices, in order to prevent paying multiple times for thesame products or services.

The disclosure and claims do not describe only a particular outcome ofinvoice de-duplication, but the disclosure and claims include specificrules for implementing the outcome of invoice de-duplication and thatrender information into a specific format that is then used and appliedto create the desired results of invoice de-duplication, as set forth inMcRO, Inc. v. Bandai Namco Games America Inc. (Fed. Cir. case number15-1080, Sep. 13, 2016). In other words, the outcome of invoicede-duplication can be performed by many different types of rules andcombinations of rules, and this disclosure includes various embodimentswith specific rules. While the absence of complete preemption may notguarantee that a claim is eligible, the disclosure does not sufficientlypreempt the field of invoice de-duplication at all. The disclosure actsto narrow, confine, and otherwise tie down the disclosure so as not tocover the general abstract idea of just invoice de-duplication.Significantly, other systems and methods exist for invoicede-duplication, so it would be inappropriate to assert that the claimedinvention preempts the field or monopolizes the basic tools of invoicede-duplication. In other words, the disclosure will not prevent othersfrom invoice de-duplication, because other systems are alreadyperforming the functionality in different ways than the claimedinvention. Moreover, the claimed invention includes an inventive conceptthat may be found in the non-conventional and non-generic arrangement ofknown, conventional pieces, in conformance with Bascom v. AT&T Mobility,2015-1763 (Fed. Cir. 2016). The disclosure and claims go way beyond anyconventionality of any one of the systems in that the interaction andsynergy of the systems leads to additional functionality that is notprovided by any one of the systems operating independently. Thedisclosure and claims may also include the interaction between multipledifferent systems, so the disclosure cannot be considered animplementation of a generic computer, or just “apply it” to an abstractprocess. The disclosure and claims may also be directed to improvementsto software with a specific implementation of a solution to a problem inthe software arts.

The detailed description of various embodiments herein makes referenceto the accompanying drawings and pictures, which show variousembodiments by way of illustration. While these various embodiments aredescribed in sufficient detail to enable those skilled in the art topractice the disclosure, it should be understood that other embodimentsmay be realized and that logical and mechanical changes may be madewithout departing from the spirit and scope of the disclosure. Thus, thedetailed description herein is presented for purposes of illustrationonly and not of limitation. For example, the steps recited in any of themethod or process descriptions may be executed in any order and are notlimited to the order presented. Moreover, any of the functions or stepsmay be outsourced to or performed by one or more third parties.Modifications, additions, or omissions may be made to the systems,apparatuses, and methods described herein without departing from thescope of the disclosure. For example, the components of the systems andapparatuses may be integrated or separated. Moreover, the operations ofthe systems and apparatuses disclosed herein may be performed by more,fewer, or other components and the methods described may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order. As used in this document, “each” refers to each memberof a set or each member of a subset of a set. Furthermore, any referenceto singular includes plural embodiments, and any reference to more thanone component may include a singular embodiment. Although specificadvantages have been enumerated herein, various embodiments may includesome, none, or all of the enumerated advantages.

Systems, methods, and computer program products are provided. In thedetailed description herein, references to “various embodiments,” “oneembodiment,” “an embodiment,” “an example embodiment,” etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described. After reading the description, itwill be apparent to one skilled in the relevant art(s) how to implementthe disclosure in alternative embodiments.

As used herein, “satisfy,” “meet,” “match,” “associated with”, orsimilar phrases may include an identical match, a partial match, meetingcertain criteria, matching a subset of data, a correlation, satisfyingcertain criteria, a correspondence, an association, an algorithmicrelationship, and/or the like. Similarly, as used herein, “authenticate”or similar terms may include an exact authentication, a partialauthentication, authenticating a subset of data, a correspondence,satisfying certain criteria, an association, an algorithmicrelationship, and/or the like.

The term “non-transitory” is to be understood to remove only propagatingtransitory signals per se from the claim scope and does not relinquishrights to all standard computer-readable media that are not onlypropagating transitory signals per se. Stated another way, the meaningof the term “non-transitory computer-readable medium” and“non-transitory computer-readable storage medium” should be construed toexclude only those types of transitory computer-readable media whichwere found in In re Nuijten to fall outside the scope of patentablesubject matter under 35 U.S.C. § 101.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any elements that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of the disclosure. The scope of the disclosure isaccordingly limited by nothing other than the appended claims, in whichreference to an element in the singular is not intended to mean “one andonly one” unless explicitly so stated, but rather “one or more.”Moreover, where a phrase similar to ‘at least one of A, B, and C’ or ‘atleast one of A, B, or C’ is used in the claims or specification, it isintended that the phrase be interpreted to mean that A alone may bepresent in an embodiment, B alone may be present in an embodiment, Calone may be present in an embodiment, or that any combination of theelements A, B and C may be present in a single embodiment; for example,A and B, A and C, B and C, or A and B and C. Although the disclosureincludes a method, it is contemplated that it may be embodied ascomputer program instructions on a tangible computer-readable carrier,such as a magnetic or optical memory or a magnetic or optical disk. Allstructural, chemical, and functional equivalents to the elements of theabove-described various embodiments that are known to those of ordinaryskill in the art are expressly incorporated herein by reference and areintended to be encompassed by the present claims. Moreover, it is notnecessary for a device or method to address each and every problemsought to be solved by the present disclosure, for it to be encompassedby the present claims. Furthermore, no element, component, or methodstep in the present disclosure is intended to be dedicated to the publicregardless of whether the element, component, or method step isexplicitly recited in the claims. No claim element is intended to invoke35 U.S.C. § 112(f) unless the element is expressly recited using thephrase “means for” or “step for”. As used herein, the terms “comprises,”“comprising,” or any other variation thereof, are intended to cover anon-exclusive inclusion, such that a process, method, article, orapparatus that comprises a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus.

Computer programs (also referred to as computer control logic) arestored in main memory and/or secondary memory. Computer programs mayalso be received via communications interface. Such computer programs,when executed, enable the computer system to perform the features asdiscussed herein. In particular, the computer programs, when executed,enable the processor to perform the features of various embodiments.Accordingly, such computer programs represent controllers of thecomputer system.

For the sake of brevity, conventional data networking, applicationdevelopment, and other functional aspects of the systems (and componentsof the individual operating components of the systems) may not bedescribed in detail herein. Furthermore, the connecting lines shown inthe various figures contained herein are intended to represent exemplaryfunctional relationships and/or physical couplings between the variouselements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

In various embodiments, the methods described herein are implementedusing the various particular machines described herein. The methodsdescribed herein may be implemented using the below particular machines,and those hereinafter developed, in any suitable combination, as wouldbe appreciated immediately by one skilled in the art. Further, as isunambiguous from this disclosure, the methods described herein mayresult in various transformations of certain articles.

The various system components discussed herein may include one or moreof the following: a host server or other computing systems including aprocessor for processing digital data; a memory coupled to the processorfor storing digital data; an input digitizer coupled to the processorfor inputting digital data; an application program stored in the memoryand accessible by the processor for directing processing of digital databy the processor; a display device coupled to the processor and memoryfor displaying information derived from digital data processed by theprocessor; and a plurality of databases. Various databases used hereinmay include: client data; merchant data; financial institution data;and/or like data useful in the operation of the system. As those skilledin the art will appreciate, user computer may include an operatingsystem (e.g., WINDOWS®, UNIX®, LINUX®, SOLARIS®, MACOS®, etc.) as wellas various conventional support software and drivers typicallyassociated with computers.

The present system or any part(s) or function(s) thereof may beimplemented using hardware, software, or a combination thereof and maybe implemented in one or more computer systems or other processingsystems. However, the manipulations performed by embodiments were oftenreferred to in terms, such as matching or selecting, which are commonlyassociated with mental operations performed by a human operator. No suchcapability of a human operator is necessary, or desirable in most cases,in any of the operations described herein. Rather, the operations may bemachine operations or any of the operations may be conducted or enhancedby artificial intelligence (AI) or machine learning. Artificialintelligence may refer generally to the study of agents (e.g., machines,computer-based systems, etc.) that perceive the world around them, formplans, and make decisions to achieve their goals. Foundations of AIinclude mathematics, logic, philosophy, probability, linguistics,neuroscience, and decision theory. Many fields fall under the umbrellaof AI, such as computer vision, robotics, machine learning, and naturallanguage processing. Useful machines for performing the variousembodiments include general purpose digital computers or similardevices.

In various embodiments, the embodiments are directed toward one or morecomputer systems capable of carrying out the functionalities describedherein. The computer system includes one or more processors. Theprocessor is connected to a communication infrastructure (e.g., acommunications bus, cross-over bar, network, etc.). Various softwareembodiments are described in terms of this exemplary computer system.After reading this description, it will become apparent to a personskilled in the relevant art(s) how to implement various embodimentsusing other computer systems and/or architectures. The computer systemcan include a display interface that forwards graphics, text, and otherdata from the communication infrastructure (or from a frame buffer notshown) for display on a display unit.

The terms “computer program medium,” “computer usable medium,” and“computer readable medium” are used to generally refer to media such asremovable storage drive and a hard disk installed in hard disk drive.These computer program products provide software to a computer system.

In various embodiments, the servers described herein may includeapplication servers (e.g. WEBSPHERE®, WEBLOGIC®, JBOSS®, POSTGRES PLUSADVANCED SERVER®, etc.). In various embodiments, the server may includeweb servers (e.g. Apache, IIS, GOOGLE® Web Server, SUN JAVA® System WebServer, JAVA® Virtual Machine running on LINUX® or WINDOWS® operatingsystems).

The various system components may be independently, separately, orcollectively suitably coupled to the network via data links whichincludes, for example, a connection to an Internet Service Provider(ISP) over the local loop as is typically used in connection withstandard modem communication, cable modem, DISH NETWORK®, ISDN, DigitalSubscriber Line (DSL), or various wireless communication methods, see,e.g., Gilbert Held, Understanding Data Communications (1996), which ishereby incorporated by reference. It is noted that the network may beimplemented as other types of networks, such as an interactivetelevision (ITV) network. Moreover, the system contemplates the use,sale, or distribution of any goods, services, or information over anynetwork having similar functionality described herein.

Any databases discussed herein may include relational, hierarchical,graphical, blockchain, object-oriented structure, and/or any otherdatabase configurations. Any database may also include a flat filestructure wherein data may be stored in a single file in the form ofrows and columns, with no structure for indexing and no structuralrelationships between records. For example, a flat file structure mayinclude a delimited text file, a CSV (comma-separated values) file,and/or any other suitable flat file structure. Common database productsthat may be used to implement the databases include DB2® by IBM®(Armonk, N.Y.), various database products available from ORACLE®Corporation (Redwood Shores, Calif.), MICROSOFT ACCESS® or MICROSOFT SQLSERVER® by MICROSOFT® Corporation (Redmond, Wash.), MYSQL® by MySQL AB(Uppsala, Sweden), MONGODB®, Redis, Apache Cassandra®, HBASE® byAPACHE®, MapR-DB by the MAPR® corporation, or any other suitabledatabase product. Moreover, any database may be organized in anysuitable manner, for example, as data tables or lookup tables. Eachrecord may be a single file, a series of files, a linked series of datafields, or any other data structure.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, devices, servers, or other components of thesystem may consist of any combination thereof at a single location or atmultiple locations, wherein each database or system includes any ofvarious suitable security features, such as firewalls, access codes,encryption, decryption, compression, decompression, and/or the like.

Any database discussed herein may comprise a distributed ledgermaintained by a plurality of computing devices (e.g., nodes) over apeer-to-peer network. Each computing device maintains a copy and/orpartial copy of the distributed ledger and communicates with one or moreother computing devices in the network to validate and write data to thedistributed ledger. The distributed ledger may use features andfunctionality of blockchain technology, including, for example,consensus based validation, immutability, and cryptographically chainedblocks of data. The blockchain may comprise a ledger of interconnectedblocks containing data. The blockchain may provide enhanced securitybecause each block may hold individual transactions and the results ofany blockchain executables. Each block may link to the previous blockand may include a timestamp. Blocks may be linked because each block mayinclude the hash of the prior block in the blockchain. The linked blocksform a chain, with only one successor block allowed to link to one otherpredecessor block for a single chain. Forks may be possible wheredivergent chains are established from a previously uniform blockchain,though typically only one of the divergent chains will be maintained asthe consensus chain. In various embodiments, the blockchain mayimplement smart contracts that enforce data workflows in a decentralizedmanner. The system may also include applications deployed on userdevices such as, for example, computers, tablets, smartphones, Internetof Things devices (“IoT” devices), etc. The applications may communicatewith the blockchain (e.g., directly or via a blockchain node) totransmit and retrieve data. In various embodiments, a governingorganization or consortium may control access to data stored on theblockchain. Registration with the managing organization(s) may enableparticipation in the blockchain network. For more information ondistributed ledgers implementing features and functionalities ofblockchain, see U.S. application Ser. No. 15/266,350 titled SYSTEMS ANDMETHODS FOR BLOCKCHAIN BASED PAYMENT NETWORKS and filed on Sep. 15,2016, U.S. application Ser. No. 15/682,180 titled SYSTEMS AND METHODSFOR DATA FILE TRANSFER BALANCING AND CONTROL ON BLOCKCHAIN and filedAug. 21, 2017, U.S. application Ser. No. 15/728,086 titled SYSTEMS ANDMETHODS FOR LOYALTY POINT DISTRIBUTION and filed Oct. 9, 2017, U.S.application Ser. No. 15/785,843 titled MESSAGING BALANCING AND CONTROLON BLOCKCHAIN and filed on Oct. 17, 2017, U.S. application Ser. No.15/785,870 titled API REQUEST AND RESPONSE BALANCING AND CONTROL ONBLOCKCHAIN and filed on Oct. 17, 2017, U.S. application Ser. No.15/824,450 titled SINGLE SIGN-ON SOLUTION USING BLOCKCHAIN and filed onNov. 28, 2017, U.S. application Ser. No. 15/824,513 titled TRANSACTIONAUTHORIZATION PROCESS USING BLOCKCHAIN and filed on Nov. 28, 2017, U.S.application Ser. No. 15/943,168 titled TRANSACTION PROCESS USINGBLOCKCHAIN TOKEN SMART CONTRACTS and filed on Apr. 2, 2018, and U.S.application Ser. No. 15/943,271 titled FRAUD MANAGEMENT USING ADISTRIBUTED DATABASE and filed on Apr. 2, 2018, the contents of whichare each incorporated by reference in its entirety.

Phrases and terms similar to “merchant,” “supplier” or “seller” mayinclude any entity that receives payment or other consideration. Forexample, a supplier may request payment for goods sold to a buyer whoholds an account with a transaction account issuer.

Phrases and terms similar to a “buyer” may include any entity thatreceives goods or services in exchange for consideration (e.g.,financial payment). For example, a buyer may purchase, lease, rent,barter or otherwise obtain goods from a supplier and pay the supplierusing a transaction account.

Therefore, the following is claimed:
 1. A computer-implemented method, comprising: receiving an invoice; identifying unstructured text in the invoice; converting the unstructured text in the invoice to structured text; and generating a structure preserved layout of the invoice that comprises the structured text.
 2. The computer-implemented method of claim 1, wherein the invoice is received in a first format; and the computer-implemented further comprises: converting the invoice to a second format in the form of an image file, and wherein identifying the unstructured text in the invoice further comprises performing optical character recognition on the image file to identify the unstructured text in the invoice.
 3. The computer-implemented method of claim 1, further comprising: removing all spaces from the structured text to generate an intermediate text; segmenting non-space separated words in the intermediate text based at least in part on a reference dictionary to generate a clean text.
 4. The computer-implemented method of claim 1, further comprising: identifying a plurality of bigrams in the structured text; and identifying a plurality of similar bigrams based on a reference of the plurality of bigrams to a language model database; and replacing the plurality of bigrams with the plurality of similar bigrams to generate a clean text.
 5. The computer-implemented method of claim 1, wherein the invoice is a first invoice and the computer-implemented method further comprises comparing the structured text of the first invoice with structured text of a second invoice to determine that the first invoice and the second invoice are duplicates.
 6. The computer-implemented method of claim 1, further comprising extracting key-value pairs from the structure preserved layout.
 7. The computer-implemented method of claim 1, further comprising saving the structure preserved layout as a comma separated value (CSV) file.
 8. A system, comprising: a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: receive an invoice; identify unstructured text in the invoice; convert the unstructured text in the invoice to structured text; and generate a structure preserved layout of the invoice that comprises the structured text.
 9. The system of claim 8, wherein the invoice is received in a first format; and the machine-readable instructions, when executed by the processor, further cause the computing device to at least: convert the invoice to a second format in the form of an image file, and wherein the machine-readable instructions that cause the computing device to identify the unstructured text in the invoice further cause the computing device to perform optical character recognition on the image file to identify the unstructured text in the invoice.
 10. The system of claim 8, wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least: remove all spaces from the structured text to generate an intermediate text; segment non-space separated words in the intermediate text based at least in part on a reference dictionary to generate a clean text.
 11. The system of claim 8, wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least: identify a plurality of bigrams in the structured text; and identify a plurality of similar bigrams based on a reference of the plurality of bigrams to a language model database; and replace the plurality of bigrams with the plurality of similar bigrams to generate a clean text.
 12. The system of claim 8, wherein the invoice is a first invoice and the machine-readable instructions, when executed by the processor, further cause the computing device to at least compare the structured text of the first invoice with structured text of a second invoice to determine that the first invoice and the second invoice are duplicates.
 13. The system of claim 8, wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least extract key-value pairs from the structure preserved layout.
 14. The system of claim 8 wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least save the structure preserved layout as a comma separated value (CSV) file.
 15. A non-transitory, computer-readable medium comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least: receive an invoice; identify unstructured text in the invoice; convert the unstructured text in the invoice to structured text; and generate a structure preserved layout of the invoice that comprises the structured text.
 16. The non-transitory, computer-readable medium of claim 15, wherein the invoice is received in a first format; and the machine-readable instructions, when executed by the processor, further cause the computing device to at least: convert the invoice to a second format in the form of an image file, and wherein the machine-readable instructions that cause the computing device to identify the unstructured text in the invoice further cause the computing device to perform optical character recognition on the image file to identify the unstructured text in the invoice.
 17. The non-transitory, computer-readable medium of claim 15, wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least: remove all spaces from the structured text to generate an intermediate text; segment non-space separated words in the intermediate text based at least in part on a reference dictionary to generate a clean text.
 18. The non-transitory, computer-readable medium of claim 15, wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least: identify a plurality of bigrams in the structured text; and identify a plurality of similar bigrams based on a reference of the plurality of bigrams to a language model database; and replace the plurality of bigrams with the plurality of similar bigrams to generate a clean text.
 19. The non-transitory, computer-readable medium of claim 15, wherein the invoice is a first invoice and the machine-readable instructions, when executed by the processor, further cause the computing device to at least compare the structured text of the first invoice with structured text of a second invoice to determine that the first invoice and the second invoice are duplicates.
 20. The non-transitory, computer-readable medium of claim 15, wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least extract key-value pairs from the structure preserved layout. 